Journal Description
Digital
Digital
is an international, peer-reviewed, open access journal on digital technologies and digital application, particularly with how such technologies affect our health, education and economy, published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, EBSCO, and other databases.
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 22.7 days after submission; acceptance to publication is undertaken in 3.5 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
Latest Articles
Creating Location-Based Mobile Applications for Tourism: A Virtual AR Guide for Western Macedonia
Digital 2024, 4(1), 271-301; https://doi.org/10.3390/digital4010014 - 01 Mar 2024
Abstract
Augmented reality (AR) applications are currently used in many fields for communication and educational purposes. Tourism is also a sector where augmented reality is used for destination marketing and cultural heritage promotion. This study will focus on mobile location-based AR applications and their
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Augmented reality (AR) applications are currently used in many fields for communication and educational purposes. Tourism is also a sector where augmented reality is used for destination marketing and cultural heritage promotion. This study will focus on mobile location-based AR applications and their potential in tourism. Such applications can guide tourists to places of interest and enhance their overall experience. The aim of this paper is to present a mobile application that was created for tourists visiting the region of Western Macedonia, Greece. The application was developed in order to guide the users in the region, entertain them, and educate them about the region’s sights, cultural heritage, and other special characteristics. The paper also aims to present a large set of features that are present in the application, including various types of AR (marker-based, markerless, and location-based) in order to provide designers who wish to create AR applications for tourism with new ideas. The application was validated by a usability test, and its features were evaluated by 39 participants who completed a questionnaire with 29 Likert-scale items. This procedure revealed the level of acceptance for the application features, and valuable feedback was also received during a discussion with the participants about how the application could be upgraded in the future.
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(This article belongs to the Collection Digital Systems for Tourism)
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Open AccessReview
Decoding the Relationship of Artificial Intelligence, Advertising, and Generative Models
by
Camille Velasco Lim, Yu-Peng Zhu, Muhammad Omar and Han-Woo Park
Digital 2024, 4(1), 244-270; https://doi.org/10.3390/digital4010013 - 01 Mar 2024
Abstract
Although artificial intelligence technologies have provided valuable insights into the advertising industry, more comprehensive studies that properly examine the applications of AI in advertising using scientometric network analysis are needed. Using publications from journals indexed in the Web of Science, we seek to
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Although artificial intelligence technologies have provided valuable insights into the advertising industry, more comprehensive studies that properly examine the applications of AI in advertising using scientometric network analysis are needed. Using publications from journals indexed in the Web of Science, we seek to analyze the emergence of AI through the examination of keyword co-occurrences and co-authorship. Our goal is to identify essential concepts and influential research that have significantly impacted the advertising business. The findings highlight noteworthy patterns, indicating the growing importance of machine learning tools and techniques such as deep learning, and advanced natural language processing methods like word2vec, GANs, and others, as well as their societal impacts as they continue to define the future of advertising practices.
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(This article belongs to the Special Issue Digital in 2024)
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Open AccessArticle
Quality Control Methods Using Quality Characteristics in Development and Operations
by
Daiju Kato and Hiroshi Ishikawa
Digital 2024, 4(1), 232-243; https://doi.org/10.3390/digital4010012 - 01 Mar 2024
Abstract
Since the Software Quality Model was defined as an international standard, many quality assurance teams have used this quality model in a waterfall model for software development and quality control. As more software is delivered as a cloud service, various methodologies have been
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Since the Software Quality Model was defined as an international standard, many quality assurance teams have used this quality model in a waterfall model for software development and quality control. As more software is delivered as a cloud service, various methodologies have been created with an awareness of the link between development productivity and operations, enabling faster development. However, most development methods are development-oriented with awareness of development progress, and there has been little consideration of methods that achieve quality orientation for continuous quality improvement and monitoring. Therefore, we developed a method to visualize the progress of software quality during development by defining quality goals in the project charter using the quality model defined in international standards, classifying each test by quality characteristics, and clarifying the quality ensured by each test. This was achieved by classifying each test by quality characteristics and clarifying the quality ensured by each test. To use quality characteristics as KPIs, it is necessary to manage test results for each test type and compare them with past build results. This paper explains how to visualize the quality to be assured and the benefits of using quality characteristics as KPIs and proposes a method to achieve rapid and high-quality product development.
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(This article belongs to the Special Issue “Management of Digital Ecosystems” Dedicated to the Memory of Prof. William I. Grosky August 4, 1944–November 13, 2020)
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Open AccessArticle
An Improved Approach for Generating Digital Twins of Cultural Spaces through the Integration of Photogrammetry and Laser Scanning Technologies
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Markos Konstantakis, Georgios Trichopoulos, John Aliprantis, Nikitas Gavogiannis, Anna Karagianni, Panos Parthenios, Konstantinos Serraos and George Caridakis
Digital 2024, 4(1), 215-231; https://doi.org/10.3390/digital4010011 - 16 Feb 2024
Abstract
The paper introduces an innovative methodology that combines photogrammetry and laser scanning techniques to create detailed 3D models of historic mansions within the Kifissia region of Attica, Greece. While photogrammetry excels in capturing intricate textures, it faces challenges such as lighting variations and
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The paper introduces an innovative methodology that combines photogrammetry and laser scanning techniques to create detailed 3D models of historic mansions within the Kifissia region of Attica, Greece. While photogrammetry excels in capturing intricate textures, it faces challenges such as lighting variations and precise image alignment. On the other hand, laser scanning offers precision in capturing geometric details but struggles with reflective surfaces and large datasets. Our study integrates these methods to leverage their strengths and address limitations, resulting in comprehensive and accurate digital twins of cultural spaces. The methodology section outlines the step-by-step process of integration, emphasizing solutions to specific challenges encountered in the study area. Preliminary results showcase the enhanced fidelity and completeness of the digital twins, demonstrating the effectiveness of the combined approach. The subsequent sections of the paper delve into a detailed presentation of the methodology, provide a comprehensive analysis of obtained results, and discuss the implications of this innovative approach in cultural preservation and broader applications.
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(This article belongs to the Special Issue Digital in 2024)
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Open AccessArticle
The Effects of Augmented Reality on Very Young Learners’ Motivation and Learning of the Alphabet and Vocabulary
by
Eleni Korosidou
Digital 2024, 4(1), 195-214; https://doi.org/10.3390/digital4010010 - 13 Feb 2024
Abstract
This study aspires to contribute some initial results to the growing area of research regarding technology potential in the field of early foreign language literacy. An experiment was conducted to examine very young learners’ alphabet and vocabulary learning and retention in an early
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This study aspires to contribute some initial results to the growing area of research regarding technology potential in the field of early foreign language literacy. An experiment was conducted to examine very young learners’ alphabet and vocabulary learning and retention in an early foreign language (FL) learning context when implementing augmented reality (AR) applications, while very young learners’ motivation was also assessed. A pilot intervention was implemented in a state school in northern Greece. The participants (n = 26) were primary school first-graders (5.5–6 years old) and were assigned into two groups, experimental (13) and control (13). To examine the effects of the intervention, this current study employed two instruments: (a) a pre-test–post-test model to assess young learners’ alphabet and vocabulary learning during three phases and (b) a questionnaire to assess their motivation during the learning process. The findings of this study reveal that both groups displayed significant improvements in FL alphabet and vocabulary learning; however, there are statistical differences in favor of the experimental group regarding long-term alphabet and vocabulary learning and retention. Furthermore, qualitative results regarding children’s perceptions of the technology used indicate that AR was highly appealing and motivating to participating students.
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(This article belongs to the Collection Multimedia-Based Digital Learning)
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Open AccessOpinion
On Enhancing the COVID-19 Certification System for the Digitally-Illiterate People Inclusion in the European Union
by
Bartłomiej Hadasik and Maria Mach-Król
Digital 2024, 4(1), 182-194; https://doi.org/10.3390/digital4010009 - 11 Feb 2024
Abstract
The COVID-19 pandemic led to widespread restrictions globally, prompting governments to implement measures for containment. Vaccines, while aiding in reducing virus transmission, have also introduced the challenge of identifying vaccinated individuals for the purpose of easing restrictions. The European Union (EU) addressed this
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The COVID-19 pandemic led to widespread restrictions globally, prompting governments to implement measures for containment. Vaccines, while aiding in reducing virus transmission, have also introduced the challenge of identifying vaccinated individuals for the purpose of easing restrictions. The European Union (EU) addressed this through the “digital COVID-19 certification” system, allowing citizens to travel within the EU based on their vaccination, recovery, or negative test status. However, the system’s digital format poses challenges for those who are not digitally proficient, such as seniors and those with low educational or socioeconomic status. This study aims to propose enhancements to the current system, considering the mobility needs of all citizens. The methodology involves reviewing literature on digital literacy, the digital divide, and information systems related to vaccination and certification. The paper presents straightforward recommendations to make the COVID-19 certificate more accessible to digitally excluded individuals. These proposals may serve as a valuable starting point for healthcare executives to evaluate and adapt the certification scheme to be inclusive of a broader range of stakeholders.
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(This article belongs to the Special Issue Digital Healthcare in Pandemics)
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Open AccessArticle
Contribution of Social Media Addiction on Intention to Buy in Social Media Sites
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Ângela Leite, Anabela Rodrigues, Ana Margarida Ribeiro and Sílvia Lopes
Digital 2024, 4(1), 169-181; https://doi.org/10.3390/digital4010008 - 01 Feb 2024
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The aim of this study is to assess whether social media addiction contributes to the intention to buy; it is based on the model of Hajli (2014) that assesses the relationships between the constructs of social media use, trust, perceived usefulness, and intention
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The aim of this study is to assess whether social media addiction contributes to the intention to buy; it is based on the model of Hajli (2014) that assesses the relationships between the constructs of social media use, trust, perceived usefulness, and intention to buy in social media sites. To this end, a confirmatory factor analysis was carried out to evaluate whether the Hajli model applied to this sample, as well as multigroup CFA to measure invariance across gender and across following influencers or not. Finally, the path analysis evaluates the intersection of social media addiction with the Hajli model (2014). The results confirmed the Hajli model as well as the inclusion in the model of social media addiction as a variable that contributes to purchase intention on social media. Configural, metric, and scalar invariance were found across genders and across the following influencers or not. Also, the values found for internal consistency and composite reliability, convergent reliability, and discriminant reliability were within the reference values.
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Open AccessArticle
Aleppo Pixelated: An Urban Reading through Digitized Historical Maps and High-Resolution Orthomosaics Case Study of al-ʿAqaba and al-Jallūm Quarters
by
Rahaf Orabi
Digital 2024, 4(1), 152-168; https://doi.org/10.3390/digital4010007 - 23 Jan 2024
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This article relies on a combination of digital and analog data to analyze the 2D urban development of al-ʿAqaba and Jallūm districts in the Old City of Aleppo. The dataset consists of vectorized historical maps of the city spanning various historical periods. The
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This article relies on a combination of digital and analog data to analyze the 2D urban development of al-ʿAqaba and Jallūm districts in the Old City of Aleppo. The dataset consists of vectorized historical maps of the city spanning various historical periods. The oldest map in the collection dates back to the 1900s. Additionally, there are high-resolution orthomosaics created from a 3D model obtained through Terrestrial Laser Scanning (TLS) and Aerial Photogrammetry techniques. Through the analysis and integration of these various data types, the article proposes an analog-digital workflow that tracks the alterations in the urban fabric of the designated study area. The analysis primarily examines the alterations in the city’s two-dimensional layout and the distribution of mass and void. Tracking the changes in the street network of the studied area is the main goal of this research, along with recognizing the spatial changes in the built environment. The article identified changes in both the open spaces and the street layout.
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Open AccessArticle
Emotions during the Pandemic’s First Wave: The Case of Greek Tweets
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Yannis Skarpelos, Sophia Messini, Elina Roinioti, Kostas Karpouzis, Stavros Kaperonis and Michaela-Gavriela Marazoti
Digital 2024, 4(1), 126-151; https://doi.org/10.3390/digital4010006 - 08 Jan 2024
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While most published research on COVID-19 focused on a few countries and especially on the second wave of the pandemic and the vaccination period, we turn to the first wave (March–May 2020) to examine the sentiments and emotions expressed by Twitter users in
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While most published research on COVID-19 focused on a few countries and especially on the second wave of the pandemic and the vaccination period, we turn to the first wave (March–May 2020) to examine the sentiments and emotions expressed by Twitter users in Greece. Using deep-learning techniques, the analysis reveals a complex interplay of surprise, anger, fear, and sadness. Initially, surprise was dominant, reflecting the shock and uncertainty accompanying the sudden onset of the pandemic. Anger replaced surprise as individuals struggled with isolation and social distancing. Despite these challenges, positive sentiments of hope, resilience and solidarity were also expressed. The COVID-19 pandemic had a strong imprint upon the emotional landscape worldwide and in Greece. This calls for appealing to emotions as well as to reason when crafting effective public health strategies.
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Open AccessArticle
Effectiveness of ChatGPT in Coding: A Comparative Analysis of Popular Large Language Models
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Carlos Eduardo Andino Coello, Mohammed Nazeh Alimam and Rand Kouatly
Digital 2024, 4(1), 114-125; https://doi.org/10.3390/digital4010005 - 08 Jan 2024
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This study explores the effectiveness and efficiency of the popular OpenAI model ChatGPT, powered by GPT-3.5 and GPT-4, in programming tasks to understand its impact on programming and potentially software development. To measure the performance of these models, a quantitative approach was employed
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This study explores the effectiveness and efficiency of the popular OpenAI model ChatGPT, powered by GPT-3.5 and GPT-4, in programming tasks to understand its impact on programming and potentially software development. To measure the performance of these models, a quantitative approach was employed using the Mostly Basic Python Problems (MBPP) dataset. In addition to the direct assessment of GPT-3.5 and GPT-4, a comparative analysis involving other popular large language models in the AI landscape, notably Google’s Bard and Anthropic’s Claude, was conducted to measure and compare their proficiency in the same tasks. The results highlight the strengths of ChatGPT models in programming tasks, offering valuable insights for the AI community, specifically for developers and researchers. As the popularity of artificial intelligence increases, this study serves as an early look into the field of AI-assisted programming.
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Open AccessArticle
Defect Isolation from Whole to Local Field Separation in Complex Interferometry Fringe Patterns through Development of Weighted Least-Squares Algorithm
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Zhenkai Chen, Wenjing Zhou, Yingjie Yu, Vivi Tornari and Gilberto Artioli
Digital 2024, 4(1), 104-113; https://doi.org/10.3390/digital4010004 - 29 Dec 2023
Abstract
In this paper, based on Gaussian 1σ-criterion and histogram segmentation, a weighted least-squares algorithm is applied and validated on digital holographic speckle pattern interferometric data to perform phase separation on the complex interference fields. The direct structural diagnosis tool is used to investigate
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In this paper, based on Gaussian 1σ-criterion and histogram segmentation, a weighted least-squares algorithm is applied and validated on digital holographic speckle pattern interferometric data to perform phase separation on the complex interference fields. The direct structural diagnosis tool is used to investigate defects and their impact on a complex antique wall painting of Giotto. The interferometry data is acquired with a portable off-axis interferometer set-up with a phase-shifted reference beam coupled with the object beam in front of the digital photosensitive medium. A digital holographic speckle pattern interferometry (DHSPI) system is used to register digital recordings of interferogram sequences over time. The surface is monitored for as long as it deforms prior to returning to its initial reference equilibrium state prior to excitation. The attempt to separate the whole vs. local defect complex amplitudes from the interferometric data is presented. The main aim is to achieve isolation and visualization of each defect’s impact amplitude in order to obtain detailed documentation of each defect and its structural impact on the surface for structural diagnosis purposes.
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(This article belongs to the Topic Research on the Application of Digital Signal Processing)
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Open AccessArticle
Bias Reduction News Recommendation System
by
Shaina Raza
Digital 2024, 4(1), 92-103; https://doi.org/10.3390/digital4010003 - 28 Dec 2023
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News recommender systems (NRS) are crucial for helping users navigate the vast amount of content available online. However, traditional NRS often suffer from biases that lead to a narrow and unfair distribution of exposure across news items. In this paper, we propose a
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News recommender systems (NRS) are crucial for helping users navigate the vast amount of content available online. However, traditional NRS often suffer from biases that lead to a narrow and unfair distribution of exposure across news items. In this paper, we propose a novel approach, the Contextual-Dual Bias Reduction Recommendation System (C-DBRRS), which leverages Long Short-Term Memory (LSTM) networks optimized with a multi-objective function to balance accuracy and diversity. We conducted experiments on two real-world news recommendation datasets and the results indicate that our approach outperforms the baseline methods, and achieves higher accuracy while promoting a fair and balanced distribution of recommendations. This work contributes to the development of a fair and responsible recommendation system.
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Open AccessArticle
Analysis of the Learning Process of Computer Programming Logic in an 8-Year-Old Elementary School Student at Home through the Scratch Program
by
Victor García
Digital 2024, 4(1), 69-91; https://doi.org/10.3390/digital4010002 - 25 Dec 2023
Abstract
This paper presents a study guide and an analysis of its use in the computer programming learning process of an 8-year-old elementary school student through the Scratch program. The research’s objective is to explore and understand how this individual student approaches learning programming
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This paper presents a study guide and an analysis of its use in the computer programming learning process of an 8-year-old elementary school student through the Scratch program. The research’s objective is to explore and understand how this individual student approaches learning programming skills and tackles challenges within the Scratch environment. An individual case study approach was adopted at home, combining qualitative and quantitative methods to gain a comprehensive insight into the student’s learning process. The study was conducted without grant support, and the researcher actively participated as an educator and observer in the student’s learning sessions. Performance was assessed, and a semi-structured interview was conducted to inquire about the student’s experiences, motivations, and interests regarding programming in Scratch, as well as their feelings after the training. Additionally, the student’s activities during programming sessions were meticulously recorded, and projects created in Scratch were analyzed to assess progress and understanding of concepts. The findings of this research have the potential to contribute to the field of programming education and provide valuable insights into how young elementary school-aged individuals can acquire computer and programming skills in an interactive environment such as Scratch. The results obtained demonstrate that using the proposed guide to introduce elementary school students to programming at home, with parents acting as educators, is feasible. Therefore, it helps facilitate access to this knowledge, which is currently limited for many individuals in an official educational setting.
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(This article belongs to the Collection Multimedia-Based Digital Learning)
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Open AccessArticle
Survey on Machine Learning Biases and Mitigation Techniques
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Sunzida Siddique, Mohd Ariful Haque, Roy George, Kishor Datta Gupta, Debashis Gupta and Md Jobair Hossain Faruk
Digital 2024, 4(1), 1-68; https://doi.org/10.3390/digital4010001 - 20 Dec 2023
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Machine learning (ML) has become increasingly prevalent in various domains. However, ML algorithms sometimes give unfair outcomes and discrimination against certain groups. Thereby, bias occurs when our results produce a decision that is systematically incorrect. At various phases of the ML pipeline, such
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Machine learning (ML) has become increasingly prevalent in various domains. However, ML algorithms sometimes give unfair outcomes and discrimination against certain groups. Thereby, bias occurs when our results produce a decision that is systematically incorrect. At various phases of the ML pipeline, such as data collection, pre-processing, model selection, and evaluation, these biases appear. Bias reduction methods for ML have been suggested using a variety of techniques. By changing the data or the model itself, adding more fairness constraints, or both, these methods try to lessen bias. The best technique relies on the particular context and application because each technique has advantages and disadvantages. Therefore, in this paper, we present a comprehensive survey of bias mitigation techniques in machine learning (ML) with a focus on in-depth exploration of methods, including adversarial training. We examine the diverse types of bias that can afflict ML systems, elucidate current research trends, and address future challenges. Our discussion encompasses a detailed analysis of pre-processing, in-processing, and post-processing methods, including their respective pros and cons. Moreover, we go beyond qualitative assessments by quantifying the strategies for bias reduction and providing empirical evidence and performance metrics. This paper serves as an invaluable resource for researchers, practitioners, and policymakers seeking to navigate the intricate landscape of bias in ML, offering both a profound understanding of the issue and actionable insights for responsible and effective bias mitigation.
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Open AccessReview
The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education
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Jon Dron
Digital 2023, 3(4), 319-335; https://doi.org/10.3390/digital3040020 - 26 Nov 2023
Cited by 1
Abstract
This paper analyzes the ways that the widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. Methodologically, the paper applies a theoretical model and grounded
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This paper analyzes the ways that the widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. Methodologically, the paper applies a theoretical model and grounded argument to present a case that GAIs are different in kind from all previous technologies. The model extends Brian Arthur’s insights into the nature of technologies as the orchestration of phenomena to our use by explaining the nature of humans’ participation in their enactment, whether as part of the orchestration (hard technique, where our roles must be performed correctly) or as orchestrators of phenomena (soft technique, performed creatively or idiosyncratically). Education may be seen as a technological process for developing these soft and hard techniques in humans to participate in the technologies, and thus the collective intelligence, of our cultures. Unlike all earlier technologies, by embodying that collective intelligence themselves, GAIs can closely emulate and implement not only the hard technique but also the soft that, until now, was humanity’s sole domain; the very things that technologies enabled us to do can now be done by the technologies themselves. Because they replace things that learners have to do in order to learn and that teachers must do in order to teach, the consequences for what, how, and even whether learning occurs are profound. The paper explores some of these consequences and concludes with theoretically informed approaches that may help us to avert some dangers while benefiting from the strengths of generative AIs. Its distinctive contributions include a novel means of understanding the distinctive differences between GAIs and all other technologies, a characterization of the nature of generative AIs as collectives (forms of collective intelligence), reasons to avoid the use of GAIs to replace teachers, and a theoretically grounded framework to guide adoption of generative AIs in education.
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(This article belongs to the Topic Education and Digital Societies for a Sustainable World)
Open AccessArticle
On the Effectiveness of Fog Offloading in a Mobility-Aware Healthcare Environment
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Ferdous Sharifi, Ali Rasaii, Amirmohammad Pasdar, Shaahin Hessabi and Young Choon Lee
Digital 2023, 3(4), 300-318; https://doi.org/10.3390/digital3040019 - 23 Nov 2023
Abstract
The emergence of fog computing has significantly enhanced real-time data processing by bringing computation resources closer to data sources. This adoption is very beneficial in the healthcare sector, where abundant time-sensitive processing tasks exist. Although such adoption is very promising, there is a
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The emergence of fog computing has significantly enhanced real-time data processing by bringing computation resources closer to data sources. This adoption is very beneficial in the healthcare sector, where abundant time-sensitive processing tasks exist. Although such adoption is very promising, there is a challenge with the limited computational capacity of fog nodes. This challenge becomes even more critical when mobile IoT nodes enter the network, potentially increasing the network load. To address this challenge, this paper presents a framework that leverages a Many-to-One offloading (M2One) policy designed for modelling the dynamic nature and time-critical aspect of processing tasks in the healthcare domain. The framework benefits the multi-tier structure of the fog layer, making efficient use of the computing capacity of mobile fog nodes to enhance the overall computing capability of the fog network. Moreover, this framework accounts for mobile IoT nodes that generate an unpredictable volume of tasks at unpredictable intervals. Under the proposed policy, a first-tier fog node, called the coordinator fog node, efficiently manages all requests offloaded by the IoT nodes and allocates them to the fog nodes. It considers factors like the limited energy in the mobile nodes, the communication channel status, and low-latency demands to distribute requests among fog nodes and meet the stringent latency requirements of healthcare applications. Through extensive simulations in a healthcare scenario, the policy’s effectiveness showed an improvement of approximately 30% in average delay compared to cloud computing and a significant reduction in network usage.
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(This article belongs to the Special Issue The Digital Transformation of Healthcare)
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Open AccessArticle
Re-Evaluating Trust and Privacy Concerns When Purchasing a Mobile App: Re-Calibrating for the Increasing Role of Artificial Intelligence
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Alex Zarifis and Shixuan Fu
Digital 2023, 3(4), 286-299; https://doi.org/10.3390/digital3040018 - 13 Oct 2023
Cited by 1
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Mobile apps utilize the features of a mobile device to offer an ever-growing range of functionalities. This vast choice of functionalities is usually available for a small fee or for free. These apps access the user’s personal data, utilizing both the sensors on
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Mobile apps utilize the features of a mobile device to offer an ever-growing range of functionalities. This vast choice of functionalities is usually available for a small fee or for free. These apps access the user’s personal data, utilizing both the sensors on the device and big data from several sources. Nowadays, Artificial Intelligence (AI) is enhancing the ability to utilize more data and gain deeper insight. This increase in the access and utilization of personal information offers benefits but also challenges to trust. Using questionnaire data from Germany, this research explores the role of trust from the consumer’s perspective when purchasing mobile apps with enhanced AI. Models of trust from e-commerce are adapted to this specific context. A model is proposed and explored with quantitative methods. Structural Equation Modeling enables the relatively complex model to be tested and supported. Propensity to trust, institution-based trust, perceived sensitivity of personal information, and trust in the mobile app are found to impact the intention to use the mobile app with enhanced AI.
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Open AccessArticle
Web-Based Malware Detection System Using Convolutional Neural Network
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Ali Alqahtani, Sumayya Azzony, Leen Alsharafi and Maha Alaseri
Digital 2023, 3(3), 273-285; https://doi.org/10.3390/digital3030017 - 12 Sep 2023
Cited by 1
Abstract
In this article, we introduce a web-based malware detection system that leverages a deep-learning approach. Our primary objective is the development of a robust deep-learning model designed for classifying malware in executable files. In contrast to conventional malware detection systems, our approach relies
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In this article, we introduce a web-based malware detection system that leverages a deep-learning approach. Our primary objective is the development of a robust deep-learning model designed for classifying malware in executable files. In contrast to conventional malware detection systems, our approach relies on static detection techniques to unveil the true nature of files as either malicious or benign. Our method makes use of a one-dimensional convolutional neural network 1D-CNN due to the nature of the portable executable file. Significantly, static analysis aligns perfectly with our objectives, allowing us to uncover static features within the portable executable header. This choice holds particular significance given the potential risks associated with dynamic detection, often necessitating the setup of controlled environments, such as virtual machines, to mitigate dangers. Moreover, we seamlessly integrate this effective deep-learning method into a web-based system, rendering it accessible and user-friendly via a web interface. Empirical evidence showcases the efficiency of our proposed methods, as demonstrated in extensive comparisons with state-of-the-art models across three diverse datasets. Our results undeniably affirm the superiority of our approach, delivering a practical, dependable, and rapid mechanism for identifying malware within executable files.
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(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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Open AccessArticle
Using Virtual Reality to Support Retrieval Practice in Blended Learning: An Interdisciplinary Professional Development Collaboration between Novice and Expert Teachers
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Pamela Cowan and Rachel Farrell
Digital 2023, 3(3), 251-272; https://doi.org/10.3390/digital3030016 - 12 Sep 2023
Abstract
This small-scale study comprised an evaluation of a teacher professional learning experience that involved the collaborative creation of resources using immersive virtual reality (VR) as a retrieval practice tool, specifically focusing on the open access aspects of the SchooVR platform. SchooVR offers teachers
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This small-scale study comprised an evaluation of a teacher professional learning experience that involved the collaborative creation of resources using immersive virtual reality (VR) as a retrieval practice tool, specifically focusing on the open access aspects of the SchooVR platform. SchooVR offers teachers and students tools to enhance teaching and learning by providing a range of virtual field trips and the ability to create customised virtual tours aligned with curriculum requirements. By leveraging the immersive 360° learning environment, learners can interact with content in meaningful ways, fostering engagement and deepening understanding. This study draws on the experiences of a group of postgraduate teacher education students and co-operating teachers in Ireland and Northern Ireland who collaborated on the creation of a number of immersive learning experiences across a range of subjects during a professional learning event. The research showcases how immersive realities, such as VR, can be integrated effectively into blended learning spaces to create resources that facilitate retrieval practice and self-paced study, thereby supporting the learning process. By embedding VR experiences into the curriculum, students are given opportunities for independent practice, review, and personalised learning tasks, all of which contribute to the consolidation of knowledge and the development of metacognitive skills. The findings suggest that SchooVR and similar immersive technologies have the potential to enhance educational experiences and promote effective learning outcomes across a variety of subject areas.
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(This article belongs to the Collection Multimedia-Based Digital Learning)
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Open AccessArticle
Teaching Data Science with Literate Programming Tools
by
Marcus Birkenkrahe
Digital 2023, 3(3), 232-250; https://doi.org/10.3390/digital3030015 - 08 Sep 2023
Abstract
This paper presents a case study on using Emacs and Org-mode for literate programming in undergraduate computer and data science courses. Over three academic terms, the author mandated these tools across courses in R, Python, C++, SQL, and more. The onboarding relied on
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This paper presents a case study on using Emacs and Org-mode for literate programming in undergraduate computer and data science courses. Over three academic terms, the author mandated these tools across courses in R, Python, C++, SQL, and more. The onboarding relied on simplified Emacs tutorials and starter configurations. Students gained proficiency after undertaking initial practice. Live coding sessions demonstrated the flexible instruction enabled by literate notebooks. Assignments and projects required documentation alongside functional code. Student feedback showed enthusiasm for learning a versatile IDE, despite some frustration with the learning curve. Skilled students highlighted efficiency gains in a unified environment. However, the uneven adoption of documentation practices pointed to a need for better incorporation into grading. Additionally, some students found Emacs unintuitive, desiring more accessible options. This highlights a need to match tools to skill levels, potentially starting novices with graphical IDEs before introducing Emacs. The key takeaways are as follows: literate programming aids comprehension but requires rigorous onboarding and reinforcement, and Emacs excels for advanced workflows but has a steep initial curve. With proper support, these tools show promise for data science education.
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(This article belongs to the Collection Multimedia-Based Digital Learning)
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